Neural heterogeneities are seen ubiquitously within the brain and greatly complicate classification efforts. Here we tested whether the responses of an anatomically well-characterized sensory neuron population to natural stimuli could be used for functional classification. To do so, we recorded from pyramidal cells within the electrosensory lateral line lobe (ELL) of the weakly electric fish Apteronotus leptorhynchus in response to natural electro-communication stimuli as these cells can be anatomically classified into six different types. We then used two independent methodologies to functionally classify responses: one relies of reducing the dimensionality of a feature space while the other directly compares the responses themselves. Both methodologies gave rise to qualitatively similar results: while ON and OFF-type cells could easily be distinguished from one another, ELL pyramidal neuron responses are actually distributed along a continuum rather than forming distinct clusters due to heterogeneities. We discuss the implications of our results for neural coding and highlight some potential advantages.

pone.0175322.g002: Responses of LS pyramidal cells to the beat.A: Peri-stimulus histograms (left) and cycle histograms (right) from six example On-type cells labeled according to phase of response to a 5Hz beat and baseline activity. Cells with higher baseline firing rates respond strongly to beats while those with lower baseline firing rates respond more weakly. Black arrows in the cycle histograms indicate the preferred phase and the length of the arrow gives the vector strength. Bin volume is indicated by values located at π/4 radians of each cycle histogram. Peak response magnitude values of example neurons are indicated by upward and downward pointing triangles on the colorbar (top) reflecting the logged stimulus driven firing rate. B: Same as in A but for six example Off-type neurons. C: Population distribution of response phase for all recordings in this study having a Z-stat ≥ 4. The histogram (bin size = π/6) reveals a bimodal distribution. Fitting the distribution with a Gaussian mixture model (black line) indicates an average on response at 1.08 radians and an average off response at 4.60 radians. The population (n = 74) is evenly divided into On- and Off-type neurons having mean vector strengths of 0.4175 ± SE 0.038 and 0.4226 ± SE 0.8664 respectively (panel inset). D: Linear regression models indicate a slight positive correlation of 0.445 exists between vector strength and baseline firing rate (p = 0.006) for On-type however no significant correlation exists for Off-type. E: No correlation exists between phase of response and baseline firing rate for either On-type or Off-type neurons as indicated by linear regression models. The rest is as in D.

Mentions:
We could easily distinguish between On- and Off-type neurons. Indeed, while On-type cells responded preferentially near the maximum (i.e., phase π/2) of the beat (Fig 2A), Off-type cells instead responded preferentially near the minimum (i.e., phase 3π/2) of the beat (Fig 2B). Plotting the distribution of the preferred phase across our dataset revealed a bimodal distribution (Hartigan’s dip test, Dip = 0.105, p = 0.001) with two well-separated modes (Fig 2C). On-type cells were assigned as belonging to the left mode (blue). This mode was centered at 1.08 radians had a kurtosis value near normality (k = 2.78) but was fairly positively skewed (s = 0.39). Off-type cells were assigned as belonging to the right mode (red). This mode was centered at 4.60 radians however had a lower value of kurtosis (k = 1.91) but was less skewed from normality (0.09). We also found a significant positive correlation between phase locking as measured by the vector strength and the baseline firing rate for On-type cells (PCC = 0.445, R2 = 0.1985, p = 0.0057, Fig 2D). In contrast, for Off-type cells, there was no significant correlation between vector strength and baseline firing rate (PCC = 0.124, R2 = 0.01534, p = 0.47, Fig 2D). We further found no significant correlation between the preferred phase and the baseline firing rate for either On- (PCC = -0.179, R2 = 0.03199, p = 0.29, Fig 2E) or Off- (PCC = 0.0467, R2 = 0.002178, p = 0.78, Fig 2E) type pyramidal cells. Overall, these results agree with previous ones [30, 41].

pone.0175322.g002: Responses of LS pyramidal cells to the beat.A: Peri-stimulus histograms (left) and cycle histograms (right) from six example On-type cells labeled according to phase of response to a 5Hz beat and baseline activity. Cells with higher baseline firing rates respond strongly to beats while those with lower baseline firing rates respond more weakly. Black arrows in the cycle histograms indicate the preferred phase and the length of the arrow gives the vector strength. Bin volume is indicated by values located at π/4 radians of each cycle histogram. Peak response magnitude values of example neurons are indicated by upward and downward pointing triangles on the colorbar (top) reflecting the logged stimulus driven firing rate. B: Same as in A but for six example Off-type neurons. C: Population distribution of response phase for all recordings in this study having a Z-stat ≥ 4. The histogram (bin size = π/6) reveals a bimodal distribution. Fitting the distribution with a Gaussian mixture model (black line) indicates an average on response at 1.08 radians and an average off response at 4.60 radians. The population (n = 74) is evenly divided into On- and Off-type neurons having mean vector strengths of 0.4175 ± SE 0.038 and 0.4226 ± SE 0.8664 respectively (panel inset). D: Linear regression models indicate a slight positive correlation of 0.445 exists between vector strength and baseline firing rate (p = 0.006) for On-type however no significant correlation exists for Off-type. E: No correlation exists between phase of response and baseline firing rate for either On-type or Off-type neurons as indicated by linear regression models. The rest is as in D.

Mentions:
We could easily distinguish between On- and Off-type neurons. Indeed, while On-type cells responded preferentially near the maximum (i.e., phase π/2) of the beat (Fig 2A), Off-type cells instead responded preferentially near the minimum (i.e., phase 3π/2) of the beat (Fig 2B). Plotting the distribution of the preferred phase across our dataset revealed a bimodal distribution (Hartigan’s dip test, Dip = 0.105, p = 0.001) with two well-separated modes (Fig 2C). On-type cells were assigned as belonging to the left mode (blue). This mode was centered at 1.08 radians had a kurtosis value near normality (k = 2.78) but was fairly positively skewed (s = 0.39). Off-type cells were assigned as belonging to the right mode (red). This mode was centered at 4.60 radians however had a lower value of kurtosis (k = 1.91) but was less skewed from normality (0.09). We also found a significant positive correlation between phase locking as measured by the vector strength and the baseline firing rate for On-type cells (PCC = 0.445, R2 = 0.1985, p = 0.0057, Fig 2D). In contrast, for Off-type cells, there was no significant correlation between vector strength and baseline firing rate (PCC = 0.124, R2 = 0.01534, p = 0.47, Fig 2D). We further found no significant correlation between the preferred phase and the baseline firing rate for either On- (PCC = -0.179, R2 = 0.03199, p = 0.29, Fig 2E) or Off- (PCC = 0.0467, R2 = 0.002178, p = 0.78, Fig 2E) type pyramidal cells. Overall, these results agree with previous ones [30, 41].

Neural heterogeneities are seen ubiquitously within the brain and greatly complicate classification efforts. Here we tested whether the responses of an anatomically well-characterized sensory neuron population to natural stimuli could be used for functional classification. To do so, we recorded from pyramidal cells within the electrosensory lateral line lobe (ELL) of the weakly electric fish Apteronotus leptorhynchus in response to natural electro-communication stimuli as these cells can be anatomically classified into six different types. We then used two independent methodologies to functionally classify responses: one relies of reducing the dimensionality of a feature space while the other directly compares the responses themselves. Both methodologies gave rise to qualitatively similar results: while ON and OFF-type cells could easily be distinguished from one another, ELL pyramidal neuron responses are actually distributed along a continuum rather than forming distinct clusters due to heterogeneities. We discuss the implications of our results for neural coding and highlight some potential advantages.